智能感知与计算研究中心知识库

Existing DeepFake detection methods focus on passive detection,
i.e.,theydetectfakefaceimagesbyexploitingtheartifactsproduced
during DeepFake manipulation. These detection-based methods
have their limitation that they only work for ex-post forensics but
cannot erase the negative influences of DeepFakes. In this work, we
propose a proactive framework for combating DeepFake before the
data manipulations. The key idea is to find a well defined substitute
latent representation to reconstruct target facial data, leading the
reconstructed face to disable the DeepFake generation. To thisend, we invert face images into latent codes with a well trained
auto-encoder, and search the adversarial face embeddings in their
neighbor with the gradient descent method. Extensive experiments
on three typical DeepFake manipulation methods, facial attribute
editing, face expression manipulation, and face swapping, have
demonstrated the effectiveness of our method in different settings.